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Long-term changes in functional connectivity improve prediction of responses to intracranial stimulation of the human brain

Lookup NU author(s): Christoforos Papasavvas, Dr Peter TaylorORCiD, Dr Yujiang WangORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Creative Commons Attribution license.Objective. Targeted electrical stimulation of the brain perturbs neural networks and modulates their rhythmic activity both at the site of stimulation and at remote brain regions. Understanding, or even predicting, this neuromodulatory effect is crucial for any therapeutic use of brain stimulation. The objective of this study was to investigate if brain network properties prior to stimulation sessions hold associative and predictive value in understanding the neuromodulatory effect of electrical stimulation in a clinical context.Approach. We analysed the stimulation responses in 131 stimulation sessions across 66 patients with focal epilepsy recorded through intracranial electroencephalogram (iEEG). We considered functional and structural connectivity features as predictors of the response at every iEEG contact. Taking advantage of multiple recordings over days, we also investigated how slow changes in interictal functional connectivity (FC) ahead of the stimulation, representing the long-term variability of FC, relate to stimulation responses.Main results. The long-term variability of FC exhibits strong association with the stimulation-induced increases in delta and theta band power. Furthermore, we show through cross-validation that long-term variability of FC improves prediction of responses above the performance of spatial predictors alone.Significance. This study highlights the importance of the slow dynamics of FC in the prediction of brain stimulation responses. Furthermore, these findings can enhance the patient-specific design of effective neuromodulatory protocols for therapeutic interventions.

Publication metadata

Author(s): Papasavvas C, Taylor PN, Wang Y

Publication type: Article

Publication status: Published

Journal: Journal of Neural Engineering

Year: 2022

Volume: 19

Issue: 2

Online publication date: 11/03/2022

Acceptance date: 15/02/2022

Date deposited: 28/03/2022

ISSN (electronic): 1741-2560

Publisher: IOP Publishing


DOI: 10.1088/1741-2552/ac5568

PubMed id: 35168208


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Funder referenceFunder name
208940/Z/17/ZWellcome Trust
210109/Z/18/ZWellcome Trust